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- This is a series of diagnostic plots that were used to evaluate how
- well a particular statistical model fits the data and explains the
- sources of variation in an Illumina 450k dataset.
- - [`mean-var-model.pdf`](mean-var-model.pdf) shows the variance trend
- modeling performed by voom, a method originally designed for
- mean-variance modeling in RNA-seq data. In this case, it models the
- mean-variance dependency induced by the logistic transform used for
- converting beta values (i.e. percent methylation) to M-values (i.e.
- ratio of methylated to unmethylated signal) in methylation data.
- Page 2 shows the mean-variance trend after fitting the model with
- the voom weights to cancel out the trend.
- - [`sample-weights.pdf`](sample-weights.pdf) Shows the results of
- limma's `arrayWeights` method, which detects and down-weights
- outlier samples, plotted against all known clinical covariates for
- those samples. Diabetes status had a significant association with
- the sample weights, indicating that the Type I diabetes samples were
- overall more consistent and had fewer outlier observations that Type
- II diabetes samples.
- - [`pcoa.pdf`](pcoa.pdf) shows a Principle Coordinate Plot (similar to
- a PCA plot) of all the samples after subtracting out the effects of
- known covariates. Points are sized by their sample weight, and a
- crosshair shows the center of mass of each group.
- - [`pval-histograms.pdf`](pval-histograms.pdf) and
- [`pval-cdf.pdf`](pval-cdf.pdf) show the p-value distributions for
- each contrast of interest, presented as a histogram and as an
- empirical cumulative distribution function. Each is annotated with
- asymptotes indicating the estimated fraction of probes affected by
- that contrast.
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